Pretest prediction of BRCA1 or BRCA2 mutation by risk counselors and the computer model BRCAPRO
Pretest prediction of BRCA1 or BRCA2 mutation by risk counselors and the computer model BRCAPRO
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DOI：
10.1093/jnci/94.11.844
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发表时间：
2002-06-05
期刊：
影响因子：
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通讯作者：
Tomlinson, G
中科院分区：
文献类型：
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作者：
Euhus, DM;Smith, KC;Tomlinson, G
背景资料：由于BRCA基因突变检测费用昂贵，有时信息量不大，而且经常与伦理和法律的问题有关，因此在检测前需要仔细选择患者。BRCA基因突变概率的估计是预测试咨询的重要组成部分，但这些估计的准确性目前尚不清楚。我们测量了8名癌症风险咨询师和计算机模型BRCAPRO在识别可能携带BRCA基因突变的家庭方面的表现。研究方法：八位癌症风险顾问和计算机模型BRCAPRO估计了从272个家系的初始样本中选择的148个家系的BRCA基因突变概率。最终样本仅限于先证者受乳腺癌或卵巢癌影响的家系，并且BRCA1和BRCA2基因测序结果明确报告为有害突变阴性或阳性。计算每位风险顾问和BRCAPRO的敏感性、特异性、阴性预测值、阳性预测值和受试者操作特征（ROC）曲线下面积。所有统计检验均为双侧检验。结果如下：使用大于10%的BRCA基因突变概率阈值，识别突变携带者的中位灵敏度为94%（范围= 81%至98%）的8个风险顾问和92%（范围= 91%至92%）的BRCAPRO。风险咨询师在该阈值的中位特异性为16%（范围= 6%至34%），BRCAPRO为32%（范围= 30%至34%）（P = 0.04）。风险顾问的ROC曲线下面积中位数为0.671（范围= 0.620至0.717），BRCAPRO为0.712（范围= 0.706至0.720）（P = 0.04）。有轻微的，但没有统计学意义，改善所有辅导员的表现措施时，BRCAPRO分配的基因突变概率信息包括与系谱。结论：有经验的风险咨询师和计算机模型BRCAPRO识别BRCA基因突变携带者的敏感性相似。由于计算机模型始终显示出上级特异性，BRCAPRO在BRCA基因突变携带者和BRCA基因突变非携带者之间的总体区分略好。
Background: Because BRCA gene mutation testing is costly, occasionally uninformative, and frequently associated with ethical and legal issues, careful patient selection is required prior to testing. Estimation of BRCA gene mutation probability is an important component of pretest counseling, but the accuracy of these estimates is currently unknown. We measured the performance of eight cancer risk counselors and of a computer model, BRCAPRO, at identifying families likely to carry a BRCA gene mutation. Methods: Eight cancer risk counselors and the computer model BRCAPRO estimated BRCA gene mutation probabilities for 148 pedigrees selected from an initial sample of 272 pedigrees. The final sample was limited to pedigrees with a proband affected by breast or ovarian cancer and BRCA1 and BRCA2 gene sequencing results unequivocally reported as negative or positive for a deleterious mutation. Sensitivity, specificity, negative predictive value, positive predictive value, and areas under receiver operator characteristics (ROC) curves were calculated for each risk counselor and for BRCAPRO. All statistical tests were two sided. Results: Using a greater-than-10% BRCA gene mutation probability threshold, the median sensitivity for identifying mutation carriers was 94% (range = 81% to 98%) for the eight risk counselors and 92% (range = 91% to 92%) for BRCAPRO. Median specificity at this threshold was 16% (range = 6% to 34%) for the risk counselors and 32% (range = 30% to 34%) for BRCAPRO (P =.04). Median area under the ROC curves was 0.671 for the risk counselors (range = 0.620 to 0.717) and 0.712 (range = 0.706 to 0.720) for BRCAPRO (P =.04). There was a slight, but not statistically significant, improvement in all counselor performance measures when BRCAPRO-assigned gene mutation probability information was included with the pedigrees. Conclusions: Sensitivity for identifying BRCA gene mutation carriers is similar for experienced risk counselors and the computer model BRCAPRO. Because the computer model consistently demonstrated superior specificity, overall discrimination between BRCA gene mutation carriers and BRCA gene mutation noncarriers was slightly better for BRCAPRO.